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Differences Across Originators in CMBS Loan Underwriting Bank Structure Conference Federal Reserve Bank of Chicago, 4 May 2011 Lamont Black, Sean Chu, Andrew Cohen, and Joseph Nichols The opinions expresses in this discussion are ours alone. They do not represent the opinions of the Board of Governors of the Federal Reserve System of its staff. 1 Introduction • CRE: “second wave” of financial crisis – High delinquency rates (9.5% of loans originated since 2005 now delinquent) – CMBS market shutdown & future refinancing waves • Like RMBS, observers cite distorted incentives for quality underwriting standards as a primary cause • At the same time, CMBS portfolios contain fewer loans, and individual loan characteristics much more transparent. • To what extent does CMBS underwriting quality vary across originator types? – 6 key types characterized by capital and corporate structure • Evidence on incentive distortions? (adverse selection, moral hazard) 2 Types of originators • • • • • • Commercial banks Insurance Investment banks Finance companies Foreign conduits Domestic conduits 3 Preview of findings • Conduits and foreign entities perform worst. • Insurance companies and commercial banks perform best. • Results hold both before and after controlling for observed loan characteristics. • Possible interpretation: originator types differ in their sources of warehouse funding, involvement in balance-sheet lending, capitalization, and investment in CMBS. 4 Data • Sample of 31,657 fixed-rate loans sold into any CMBS from 1999 to 2007 • Loan characteristics at origination • Matched originators to top holders (using NIC) and classified into 1 of the 6 types • Payment history through July 2010 5 Cumulative Delinquency Rates • Delinquency = 60+ days delinquent or in special servicing Originator Comm. Insur. Inv. Fin. Foreign Domestic Type Bank Co. Bank Co. Entity Conduit % ever 7.38% 4.68% 8.93% 8.76% 10.10% 12.89% delinquent 6 Cumulative Delinquency Rate by Originator Type Delinquent = ever 60 days late Year = year of origination 7 Differences in loans across originator types • Loan characteristics – DSCR, LTV, coupon • Delinquency rates conditioning on loan characteristics 8 Loan Characteristics at Origination Mean (Std. Dev.) DSCR Occupancy Coupon Spread Loan Amount LTV Ratio Cumul. Default Commercial Bank 1.49 94.59 1.47 9.71 68.15 7.38% (0.46) (7.37) (0.65) (14.87) (12.46) 1.49 96.02 1.55 8.15 64.45 (0.39) (6.32) (0.7) (11.34) (11.92) 1.5 94.73 1.46 10.87 69.02 (0.4) (7.54) (0.65) (16.93) (10.37) 1.45 93.33 1.57 8.68 70.16 (0.34) (7.37) (0.7) (11.01) (10.08) 1.41 94.86 1.55 8.58 70.82 (0.26) (6.98) (0.76) (12.99) (9.19) 1.39 94.13 1.63 10.36 70.56 (0.3) (7.53) (0.71) (15.75) (9.45) 1.47 94.69 1.5 9.59 68.73 (0.4) (7.28) (0.68) (14.63) (11.33) Insurance Investment Bank Finance Company Foreign Entity Domestic Conduit Total 4.68% 8.93% 8.76% 10.10% 12.89% 8.30% 9 Cox Proportional Hazards Model • Outcome: how long before a loan first became delinquent? • Hazards differ across originator types. • Controls for vintage, region, and property type. • Differences remain, even after controlling for underwriting characteristics. • Also find evidence of deterioration from early to later vintages, even after controlling for observable underwriting characteristics. 10 Cumulative Hazards Evaluated at Means Conditional on Originator Type 11 Cumulative Hazards Evaluated at Entire-Sample Means 12 Cumulative Hazards Estimated Separately by Originator Type 13 Cumulative Hazards by Vintage 14 Cumulative Hazards by Originator Type and Vintage 15 Institutional Features Affecting Underwriting (1) Warehouse loans Balance-sheet lenders Commercial bank X X Insurance company X X Investment bank X Finance company X Foreign entity X Depends Domestic conduit 16 Institutional Features Affecting Underwriting (2) • Warehouse Funding: Internal vs. External – Moral hazard: does originator hold mortgage for appreciable period of time prior to securitization? – External funding may be more costly product differentiation toward riskier loans • Balance sheet lending – Adverse selection: Does originator choose which loans to securitize? – Possible spillovers in lending technology (origination cost for given level of quality) – Capitalization: Correlated with risk preferences. 17 Institutional Features Affecting Underwriting (3) • Possible reason for difference between commercial banks vs. insurance companies: – Insurance companies have proportionally more balance-sheet CRE lending (10% vs. 5 to 8%) – Anecdotally, we know that insurance companies invested heavily in CMBS – maybe some of them collateralized by their own originations. 18 Discussion • Standard underwriting characteristics only partly explain loan performance. Despite reputation for transparency, CRE loan performance affected by originator type. • Adverse selection an often-cited cause of poor performance, but evidence suggests presence of mitigating factors among balance sheet lenders (e.g., better overall pools, higher K) • Must interpret conservatively: some sources of unobserved heterogeneity may be observed by investors. 19 Extensions • Can we test underwriting differences more directly? – Compare underwritten NOI to realized NOI by originator type. – For 6 percent of loans, rating agencies impose a haircut on DSCR (15 percent on average) or LTV (36 percent on average). • Are differences across originators reflected in pricing of CMBS securities? 20 Conclusion • Differences in loan performance across originator types, before and after controlling for underwriting characteristics. • Insurance companies and commercial banks best. • Foreign entities and conduits worst. • Underlying drivers behind these differences merits further study. 21 Extra slides 22 Average Debt-Service Coverage Ratio by Originator Type 23 Average Occupancy by Originator Type Insurance companies have higher occupancy over nearly all years, especially in the late 2000’s. 24 Average Loan-to-Value by Originator Type 25 Logit Model • Outcome: whether a loan ever becomes delinquent. • Variation: distinguish between regular on-time payment and prepayment. • Older loans have had more time over which to become delinquent. Control for this using vintage. • Regress on underwriting variables, originator type, vintage (originator type)*(vintage). • Are there differences across types and vintages after controlling for underwriting variables? 26 Logit Model Results Loan Characteristics at Origination Coefficients (Standard Error) Originator Type and Vintage Effects Debt-to-Service Coverage Ratio 0.044 (0.104) -0.025*** (0.003) 0.324*** (0.039) 0.011*** (0.0012) 0.052*** (0.0033) Commercial Bank Occupancy Coupon Spread Loan Amount Loan-to-Value Ratio Insurance Investment Bank Finance Company Foreign Conduit Coefficient (Standard Error) -0.287** (0.144) 0.202** (0.082) 0.077 (0.123) 0.163* (0.086) 0.230* (0.131) Vintage <= 2004 Vintage = 2005 Vintage = 2006 Vintage = 2007 0.153* (0.091) 0.225** (0.09) -0.534*** (0.124) 27 Multinomial Logit Results • 19 percent of loans prepay, less than for RMBS. • Compared with simple logit, effects of various explanatory variables on delinquency is essentially unchanged. 28 Total Volume of Originations by Originator Type Growth of CMBS market began in mid 2003 and 2004 29 Sources • Cox, D.R. 1972. Regression Models and LifeTables. Journal of the Royal Statistical Society, Series B 34:187-220. 30 Hazard Model: Explanation of Coefficients • Coefficients of variables are in Hazard Ratio form. – For example, consider a continuous random variable xi . The hazard rate given x1,…,xn is: = h(t | x1 ,..., xi ,..., xn ) h0 (t ) exp( x1β1 + ... + xi β i + ... + xn β n ) – Now consider the hazard rate given a unit increase in xi: h(t | x1 ,..., xi= + 1,..., xn ) h0 (t ) exp( x1β1 + ... + ( xi + 1) β i + ... + xn β n ) – Thus the hratio of the two hazard rates is: (t | x ,..., x + 1,..., x ) 1 i n h(t | x1 ,..., xi ,..., xn ) = exp( β i ) 31 Hazard Model: Explanation of Coefficients • Interpretation of Results: exp( βi ) – The coefficient we report in the tables is , or the hazard ratio. – If βx > 1, then an increase in x implies a higher hazard ratio (and thus a higher probability of delinquency) – If βx < 1, then an increase in x implies a lower hazard ratio (and thus a lower probability of delinquency) 32